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Machine Learning Guide

OCDevel

Released: 2017-02-01

Free 0%

60 Episodes

Audio

Free 0%

60 Episodes

Audio

Released: 2017-02-01

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#119 in Top Podcasts > Technology

Most Recent Episode

MLG 001 Introduction

Show notes: . MLG teaches the fundamentals of machine learning and artificial intelligence. It covers intuition, models, math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your...

Time: 8:11

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Show notes: ocdevel.com/mlg/1. MLG teaches the fundamentals of machine learning and artificial intelligence. It covers intuition, models, math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.

MLG, Resources Guide Gnothi (podcast project): website, Github What is this podcast? "Middle" level overview (deeper than a bird's eye view of machine learning; higher than math equations) No math/programming experience required Who is it for

Anyone curious about machine learning fundamentals Aspiring machine learning developers Why audio?

Supplementary content for commute/exercise/chores will help solidify your book/course-work What it's not

News and Interviews: TWiML and AI, O'Reilly Data Show, Talking machines Misc Topics: Linear Digressions, Data Skeptic, Learning machines 101 iTunesU issues Planned episodes

What is AI/ML: definition, comparison, history Inspiration: automation, singularity, consciousness ML Intuition: learning basics (infer/error/train); supervised/unsupervised/reinforcement; applications Math overview: linear algebra, statistics, calculus Linear models: supervised (regression, classification); unsupervised Parts: regularization, performance evaluation, dimensionality reduction, etc Deep models: neural networks, recurrent neural networks (RNNs), convolutional neural networks (convnets/CNNs) Languages and Frameworks: Python vs R vs Java vs C/C++ vs MATLAB, etc; TensorFlow vs Torch vs Theano vs Spark, etc

Episode ID: 1000381024007

GUID: a9bf6e09-aa7e-4126-9e36-22b152419c8f

Release Date: 01/02/2017, 13:00:00

Description

Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.

Feed URL

https://machinelearningguide.libsyn.com/rss

Apple Podcasts: Customer Reviews

2023-09-02

Excellent podcast

Amazing educational resource!

2020-11-18

Equivalent to Andrew Ng course and more

This is an awesome podcast, I’ve just started ML journey and finished 25 episodes, still some more to go however I must say this podcast has been a great supplement to learning. You make it ML simple by bringing in interesting analogy. Helps to cement the high level understanding of ML concepts/elements. Thanks 🙏

2018-04-16

This is just awesome!!

This channel is a great supplement to whoever wanted to dig deeper into machine learning field. The author reveals certain blind spots of normal academic syllabus by introducing recent developments of ML algos and techniques. Most of the stuff are discussed in fair details but requires extra research to fully understand. Best thing is, the author put the link in the description so you know where to start

2017-12-14

Consciousness

Great podcast. I was listening to your podcast on consciousness. I think that the topic clearly needs philosophical input that will supplement the views you raised from cognitive and neuro science.

One point. Mental states are not identical brain states (the brain is not jubilant or sad) therefore the mind is not identical to the brain as you seem to hold.

2017-07-15

Awesome podcast

For a layman like me after reading multiple books this podcast helped connect all the things I read .